Projects delivered by The2410

The solution was designed around one betting platform for bettors and sportsbook managers. The bettor-facing area covers multiple sports, pre-match markets and live events.

It also includes odds, scores, bet slips and wallet balance.

To handle live wagering, real-time sports and score feeds were provided through API connections. The platform shows live status and current odds while sportsbook managers control events and markets.

The account and wallet centre brings registration, authentication and betting history together. It also supports deposits and withdrawals.

Payment connections can include PayPal, cards and e-wallets when required.

The platform was structured in modules so casino games and slots can be added without rebuilding the core product. Esports, virtual sports, lottery and fantasy betting can also be added later.

As a result, the design links live match information, bet confirmation and wallet activity across the main screens. Settlement and reporting areas support sportsbook operations.

The solution was designed around **Power Apps** for submitting requests, reviewing approvals, and tracking tasks. Canvas or model-driven applications provide the interface for the firm’s specific processes.

To handle approvals, Power Automate routes submissions through sequential or role-based decisions and logs the outcomes. Workflow events also trigger notifications, reminders, and task assignments.

SharePoint integration keeps list records, documents, and approval results together. The system connects SharePoint, Microsoft Lists, and Excel where the process requires them.

Special attention was paid to operational visibility. Power BI receives dataset rows, creates push datasets, and refreshes datasets for reports and dashboards.

The solution was organized as modules so new forms, automations, and integrations can be added without rebuilding the whole system. Documentation and training were included so the client team can manage the system independently.

The solution was designed around a central chatbot workspace where the agency configures **conversation flows**, natural language understanding, knowledge sources, and escalation rules. `Rasa` (an open-source framework for understanding and managing conversations) supports text- and voice-based dialogue work.

To handle customer support, a separate inbox brings automated conversations and routed work together. The support agent sees the conversation history, receives a suggested response, edits the reply, and escalates complex inquiries when needed.

The lead workspace captures and qualifies leads, assigns them to sales staff, and manages follow-up sequences. Connections to CRM systems, customer databases, and websites keep lead activity linked to business processes.

Special attention was paid to **multi-channel communication**. The system connects websites, mobile apps, and social media. `Dialogflow` (a service for building conversational interfaces) and the WhatsApp Business Platform are available when named as integrations.

The reporting workspace combines campaign activity, conversations, and leads. `dbt-core` (a tool for preparing analytics data) and `grafanalib` (a library for building Grafana dashboards) support reports and visual dashboards.

The first screen accepts a prompt or script together with images, video, audio and raw footage.

To handle generation and editing in one place, a video workspace was provided with a preview, scene list and timeline. It supports generated clips, image animation and clip replacement.

Special attention was paid to narration and accessibility. Voice settings, caption text, subtitle files and background music are managed in one control area.

The final area connects rendering with delivery. The software creates output versions in selected formats and resolutions, provides download and sharing links, and supports publishing workflows.

The system is modular, so API access, subscriptions and credits can be added later. Quotas, payments, storage and additional publishing connections can also be added without rebuilding the whole product.

The solution was designed around a central dashboard for SEO projects and websites. It brings ranking trends, technical health and backlink data into one view.

AI search visibility, campaign performance and report controls complete the dashboard.

To handle research, a keyword opportunity workspace was provided. It groups keywords, shows content gaps and SERP opportunities, and keeps ranking history available for further work.

The AI content area combines topic and keyword inputs with an article editor, optimization suggestions and translation. Schema markup, publishing controls and token usage monitoring are included.

OpenAI API provides AI-assisted content and recommendations.

Backlink work was shaped as a dedicated area for publisher opportunities, SEO metrics and outreach status. Orders, quality-control flags and live backlink tracking stay visible together.

The client report view presents rankings, PageSpeed and Core Web Vitals. It also shows SEO errors and backlink data, with PDF and CSV exports.

Shareable access supports a client with view-only permissions.

The solution was designed around a prompt and reference setup. The platform accepts text prompts, uploaded images, image URLs, and face, style, or pose references.

To handle different creative directions, the generation layer was connected to diffusion models and external AI APIs. It produces realistic, stylized, and composite images, with multiple variations from the same setup.

Special attention was paid to creative control. The platform provides style, parameter, and avatar settings where the selected generation workflow supports them.

It also includes color, template, and pose settings.

The generated image workspace shows a large preview and a variation grid. Editing tools cover styles, colors, and backgrounds, while image results remain available for download.

The solution was also organized around saved images, galleries, and generation history. An administration area manages model options, user access, and usage controls.

The solution was designed around Microsoft Excel with VBA automation, forms, drop-down lists and protected tables. This gives the distributor one controlled place for stock, order and supplier information.

To handle stock movement, a transaction entry screen records sales, purchases and stock adjustments. Running balances update from the recorded transactions.

The inventory dashboard shows current stock, reorder points and low-stock flags. Filters, slicers and refresh actions keep the view focused on the required products or periods.

The reporting area combines PivotTables, summary tables and trend charts. It covers sales, purchasing and stock-out information, with filters for review.

Special attention was paid to imports and business documents. CSV and XML files can feed the workbook, while invoices and summaries can be prepared for PDF export or email delivery.

The solution was designed around the firm’s real finance sources and rules. A first working version used typical files to check how data entered the system, how it was transformed, and where the results went.

The data connection workspace connects spreadsheets, Excel files, CSV data, and accounting or ERP sources. An approved Excel template keeps imported information organized, while validation messages and import status make problems visible.

The finance dashboard refreshes current information and presents profitability, cash flow, expenses, and calculated financial metrics. Custom reports and filters keep the view focused on the firm’s reporting needs.

The reconciliation review compares transaction totals and separates matched, unmatched, and duplicate items. Anomaly flags draw attention to discrepancies while the review status shows where checking stands.

The forecast and scenario model uses historical and current financial data to show cash-flow projections, balance-sheet projections, and scenario comparisons. The modules were added one by one so the firm could tune rules, templates, and settings before the next part was introduced.

The solution was designed around the source content, editing, rendering, and publishing stages described for the agency. The creation workspace accepts scripts, prompts, and topics.

It also accepts articles, data, and images. It accepts audio, video clips, and existing media.

To handle different production styles, the workspace assembles scenes and visuals into an editable timeline. `remotion` (a library for creating videos programmatically) supports video creation, while FFmpeg (a tool for processing and rendering media) handles media operations.

Special attention was paid to repeatable branding. The system applies logos, colours, and fonts to video outputs.

Themes, styles, and reusable editing templates are also supported.

The publishing queue brings rendered videos together with titles, descriptions, and tags. `python-youtube` (a Python wrapper for the YouTube Data API) supports YouTube publishing workflows. Automation tools coordinate rendering, scheduling, and publishing.

The studio structured the system as modules so later features, forms, automations, or integrations can be added without rebuilding the whole workspace. Documentation and training support the agency team’s day-to-day operation.

The solution was designed around a Python trading application that connects market data, strategy logic, spreadsheet inputs and broker accounts. The structure keeps the main stages in one place: prepare data, review candles, validate a strategy, execute orders and monitor positions.

To handle trading inputs, an Excel workspace was provided. It reads spreadsheet data, shows candle data and marks the updated trading data produced after execution.

The trading screen brings together selectable intervals, strategy parameters and buy and sell criteria. Broker connection status and market, limit or stop order types remain available in the same screen.

The application places orders through supported broker connections and keeps execution information available for monitoring.

Special attention was paid to strategy validation. The backtesting screen uses historical candle data, strategy parameters and results tables so the firm can compare a strategy before using it for live trading.

The system was planned as modules, leaving room for documentation, setup guidance, post-delivery support and later additions such as local processing or live-market debugging.